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Introduction Tracking progress towards Sustainable Development Goal (SDG) 3·6 of reducing traffic deaths and serious injuries poses a measurement challenge in most low-income and middle-income countries (LMICs) due to large discrepancies between reported official statistics and estimates from global health measurement studies. We assess the extent to which national population censuses and health surveys can fill the information gaps.Methods We reviewed questionnaires for nationally representative surveys and censuses conducted since 2000 in LMICs. We identified sources that provide estimates of household ownership of vehicles, incidence of traffic deaths and non-fatal injuries, and prevalence of disability.Results We identified 802 data sources from 132 LMICs. Sub-Saharan African countries accounted for 43% of all measurements. The number of measurements since 2000 was high, with 97% of the current global LMIC population having at least one measurement for vehicle ownership, 77% for deaths, 90% for non-fatal injuries and 50% for disability due to traffic injuries. Recent data (since 2010) on traffic injuries were available from far fewer countries (deaths: 21 countries; non-fatal injuries: 62 and disability: 12). However, there were many more countries with recent data on less-specific questions about unintentional or all injuries (deaths: 41 countries, non-fatal: 87, disability: 32).Conclusion Traffic injuries are substantially underreported in official statistics of most LMICs. National surveys and censuses provide a viable alternative information source, but despite a large increase in their use to monitor SDGs, traffic injury measurements have not increased. We show that relatively small modifications and additions to questions in forthcoming surveys can provide countries with a way to benchmark their existing surveillance systems and result in a substantial increase in data for tracking road traffic injuries globally.

BackgroundImproved understanding of the quality of survival of patients is crucial in evaluating trauma care, understanding recovery patterns and timeframes, and informing healthcare, social, and disability service provision. We aimed to describe the longer-term health status of seriously injured patients, identify predictors of outcome, and establish recovery trajectories by population characteristics.Methods and findingsA population-based, prospective cohort study using the Victorian State Trauma Registry (VSTR) was undertaken. We followed up 2,757 adult patients, injured between July 2011 and June 2012, through deaths registry linkage and telephone interview at 6-, 12-, 24-, and 36-months postinjury. The 3-level EuroQol 5 dimensions questionnaire (EQ-5D-3L) was collected, and mixed-effects regression modelling was used to identify predictors of outcome, and recovery trajectories, for the EQ-5D-3L items and summary score. Mean (SD) age of participants was 50.8 (21.6) years, and 72% were male. Twelve percent (n = 333) died during their hospital stay, 8.1% (n = 222) of patients died postdischarge, and 155 (7.0%) were known to have survived to 36-months postinjury but were lost to follow-up at all time points. The prevalence of reporting problems at 36-months postinjury was 37% for mobility, 21% for self-care, 47% for usual activities, 50% for pain/discomfort, and 41% for anxiety/depression. Continued improvement to 36-months postinjury was only present for the usual activities item; the adjusted relative risk (ARR) of reporting problems decreased from 6 to 12 (ARR 0.87, 95% CI: 0.83-0.90), 12 to 24 (ARR 0.94, 95% CI: 0.90-0.98), and 24 to 36 months (ARR 0.95, 95% CI: 0.95-0.99). The risk of reporting problems with pain or discomfort increased from 24- to 36-months postinjury (ARR 1.06, 95% CI: 1.01, 1.12). While loss to follow-up was low, there was responder bias with patients injured in intentional events, younger, and less seriously injured patients less likely to participate; therefore, these patient subgroups were underrepresented in the study findings.ConclusionsThe prevalence of ongoing problems at 3-years postinjury is high, confirming that serious injury is frequently a chronic disorder. These findings have implications for trauma system design. Investment in interventions to reduce the longer-term impact of injuries is needed, and greater investment in primary prevention is needed.

To determine associations between the number of injuries sustained and three measures of disability 12-months post-injury for hospitalised patients.Data from 27,840 adult (18+ years) participants, hospitalised for injury, were extracted for analysis from the Validating and Improving injury Burden Estimates (Injury-VIBES) Study. Modified Poisson and linear regression analyses were used to estimate relative risks and mean differences, respectively, for a range of outcomes (Glasgow Outcome Scale-Extended, GOS-E; EQ-5D and 12-item Short Form health survey physical and mental component summary scores, PCS-12 and MCS-12) according to the number of injuries sustained, adjusted for age, sex and contributing study.More than half (54%) of patients had an injury to more than one ICD-10 body region and 62% had sustained more than one Global Burden of Disease injury type. The adjusted relative risk of a poor functional recovery (GOS-E

BackgroundInjury is a leading cause of the global burden of disease (GBD). Estimates of non-fatal injury burden have been limited by a paucity of empirical outcomes data. This study aimed to (i) establish the 12-month disability associated with each GBD 2010 injury health state, and (ii) compare approaches to modelling the impact of multiple injury health states on disability as measured by the Glasgow Outcome Scale - Extended (GOS-E).Methods12-month functional outcomes for 11,337 survivors to hospital discharge were drawn from the Victorian State Trauma Registry and the Victorian Orthopaedic Trauma Outcomes Registry. ICD-10 diagnosis codes were mapped to the GBD 2010 injury health states. Cases with a GOS-E score >6 were defined as "recovered." A split dataset approach was used. Cases were randomly assigned to development or test datasets. Probability of recovery for each health state was calculated using the development dataset. Three logistic regression models were evaluated: a) additive, multivariable; b) "worst injury;" and c) multiplicative. Models were adjusted for age and comorbidity and investigated for discrimination and calibration.FindingsA single injury health state was recorded for 46% of cases (1-16 health states per case). The additive (C-statistic 0.70, 95% CI: 0.69, 0.71) and "worst injury" (C-statistic 0.70; 95% CI: 0.68, 0.71) models demonstrated higher discrimination than the multiplicative (C-statistic 0.68; 95% CI: 0.67, 0.70) model. The additive and "worst injury" models demonstrated acceptable calibration.ConclusionsThe majority of patients survived with persisting disability at 12-months, highlighting the importance of improving estimates of non-fatal injury burden. Additive and "worst" injury models performed similarly. GBD 2010 injury states were moderately predictive of recovery 1-year post-injury. Further evaluation using additional measures of health status and functioning and comparison with the GBD 2010 disability weights will be needed to optimise injury states for future GBD studies.

OBJECTIVE: To assess the availability and quality of global death registration data used for estimating injury mortality. METHODS: The completeness and coverage of recent national death registration data from the World Health Organization mortality database were assessed. The quality of data on a specific cause of injury death was judged high if fewer than 20% of deaths were attributed to any of several partially specified causes of injury, such as "unspecified unintentional injury". FINDINGS: Recent death registration data were available for 83 countries, comprising 28% of the global population. They included most high-income countries, most countries in Latin America and several in central Asia and the Caribbean. Categories commonly used for partially specified external causes of injury resulting in death included "undetermined intent," "unspecified mechanism of unintentional injury," "unspecified road injury" and "unspecified mechanism of homicide". Only 20 countries had high-quality data. Nevertheless, because the partially specified categories do contain some information about injury mechanisms, reliable estimates of deaths due to specific external causes of injury, such as road injury, suicide and homicide, could be derived for many more countries. CONCLUSION: Only 20 countries had high-quality death registration data that could be used for estimating injury mortality because injury deaths were frequently classified using imprecise partially specified categories. Analytical methods that can derive national estimates of injury mortality from alternative data sources are needed for countries without reliable death registration systems.

Abstract ICD-11 provides a promising new way to capture healthcare-related harm or injury. In this paper, we elaborate on the framework for describing healthcare-related events where there is a presumed causal link between an event and underlying healthcare-related factors. The three-part model for describing healthcare-related harm or injury in ICD-11 consists of (1) a healthcare-related activity that is the cause of injury or other harm (selected from Chapter 23 of ICD-11); (2) a mode or mechanism of injury or harm, related to the underlying cause (also from Chapter 23 of ICD-11); and (3) the harmful consequences of the event to the patient, selected from any of Chapters 1 through 22 of ICD-11 (most importantly, the injury or harm experienced by the patient). Concepts from these three elements are linked/clustered through postcoordination to reflect the three-part model in a single coded expression. ICD-11 contains many novel features, and the three-part model described here for healthcare-related adverse events is a notable example.

Abstract Objective: Low rates of driver licensing have been linked to increased risk of transport‐related injury, and reduced access to health services, employment and educational opportunities in the Aboriginal population. This paper reports on how barriers to obtaining a driver licence are being addressed in four Aboriginal communities in New South Wales and South Australia. Methods: Qualitative data were collected over a four‐month period in 2013. Interviews with Aboriginal and non‐Aboriginal stakeholders (n=31) and 11 focus groups with Aboriginal participants (n=46) were analysed thematically using a framework approach. Results: Factors facilitating licensing included: family support, professional lessons, alternative testing and programs that assist with literacy, fines management, financial assistance and access to a supervising driver. Stakeholders recommended raising awareness of existing services and funding community‐based service provision to promote access to licensing. Discussion: Facilitating licence participation requires systemic change and long‐term investment to ensure interagency collaboration, service use and sustainability of relevant programs, including job search agencies. Implications for public health: The disadvantage faced by Aboriginal people in driver licensing is a fundamental barrier to participation and a social determinant of health. Understanding the factors that promote licensing is crucial to improving access for under‐serviced populations; recommendations provide pragmatic solutions to address licensing disadvantage.

Abstract Objective: Education, employment and equitable access to services are commonly accepted as important underlying social determinants of health. For most Australians, access to health, education and other services is facilitated by private transport and a driver licence. This study aimed to examine licensing rates and predictors of licensing in a sample of Aboriginal and Torres Strait Islander people, as these have previously been poorly described. Methods: Interviewer‐administered surveys were conducted with 625 people 16 years or older in four Aboriginal Community Controlled Health Services in New South Wales and South Australia over a two‐week period in 2012–2013. Results: Licensing rates varied from 51% to 77% by site. Compared to not having a licence, having a driver licence was significantly associated with higher odds of full‐time employment (adjusted OR 4.0, 95%CI 2.5–6.3) and educational attainment (adjusted OR 1.9, 95%CI 1.2–2.8 for trade or certificate; adjusted OR 4.0, 95%CI 1.6–9.5 for degree qualification). Conclusions: Variation in driver licensing rates suggests different yet pervasive barriers to access. There is a strong association between driver licensing, education and employment. Implications: Licensing inequality has far‐reaching impacts on the broader health and wellbeing of Aboriginal and Torres Strait Islander people, reinforcing the need for appropriate and accessible pathways to achieving and maintaining driver licensing.

Abstract Background The International Classification of Diseases (ICD) has long been the main basis for comparability of statistics on causes of mortality and morbidity between places and over time. This paper provides an overview of the recently completed 11th revision of the ICD, focusing on the main innovations and their implications. Main text Changes in content reflect knowledge and perspectives on diseases and their causes that have emerged since ICD-10 was developed about 30 years ago. Changes in design and structure reflect the arrival of the networked digital era, for which ICD-11 has been prepared. ICD-11’s information framework comprises a semantic knowledge base (the Foundation), a biomedical ontology linked to the Foundation and classifications derived from the Foundation. ICD-11 for Mortality and Morbidity Statistics (ICD-11-MMS) is the primary derived classification and the main successor to ICD-10. Innovations enabled by the new architecture include an online coding tool (replacing the index and providing additional functions), an application program interface to enable remote access to ICD-11 content and services, enhanced capability to capture and combine clinically relevant characteristics of cases and integrated support for multiple languages. Conclusions ICD-11 was adopted by the World Health Assembly in May 2019. Transition to implementation is in progress. ICD-11 can be accessed at icd.who.int.